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Since the late of 1990s, the term business intelligence (BI) and its application has been widely known and used in organizations, especially in large enterprises. But in a decade later, they started to realize that changing business environment will needs something more than just BI, which now called business analytics. In 2006, an author named Thomas wrote an article on HBR entitled “Competing on Analytics” which provisions the rising needs for business analytics. Davenport started his explanation on competing analytics by giving some examples on the successful usage of killer apps in some organizations, named Amazon, Harrah’s, Capital One and Boston Red Sox. By utilizing analytics, these organizations are able to knows better about the values that customer want, which inturn be able to squeeze all the value from the processes and make the best out of it. Davenport also point out that, to be an analytics competitor, top-down approach from the senior leadership team, as well as hiring the best people are necessary. Nonetheless, not all organizations are succesfull on using business analytics due to its characteristic. The rest of the articles explains about what organizations can make the best of analytics, as well as the changes that an organization must undergo to adopt it.

Some traditional organizations may not be fully suitable with competing analytics. One best practice that an organization my want to know is how Marriot International using analytics. But, it will not work to some traditional organizations. Davenport’s study found three key attributes that an organization must have:

WIDESPREAD USE OF MODELLING AND OPTIMIZATION

Analytics competitors do things beyond statistics and spreadsheets. They are using sort of things that could provide them better insights from data, such as:

Predictive modelling to identify the most profitable customer.

Data warehouse to pool inhous and outside data.

Optimized supply chain.

Real-time pricing.

Sophisticated experiments to calculate impact.

Some analytics competitors, especially inscurance company, like Capital One and Progessive doing series comprehensive experiments to have the best value based on their customers need, even with high-risk.

AN ENTERPRISE APPROACH

Successfull analytics competitor will implement analytics using multiple applications in wide busines functions rather than using single app. For some companies such as UPS, Capital One and Barclays Bank are already implementing business intelligence and then shifting towards full-bore analytics competitors. However, Devenport thinks that BI still have some flaws where its still use data which spreads all over the organization. The data may contains errors and make the decision inacurrate, which in contrast, analytics competitors are using centralized function to manage critical data. People within the organization is as important as the technology. Some organization like P&G create a pool of experts from various function to do the analytics.

SENIOR EXCECUTIVE ADVOCATES

Changing into an analytics competitor simply changes the organization, and it will require leadership skills to guide the organization towards sucessfull adoption. Its proven that if the initiative just pushed by one-or-two business unit leaders, it will not successfull. There was some key leadership qualities that the article pointed out, such as: appreciation and familiarity with analytics or analytics-minded, intuitive, and have the guts to make decision even not supported by numbers.

Even if an organization have the ability, it is necessary to have certain focus on only a few analytics subjects. Becoming to diffuse can make the organization losing clear sight on the purpose of analytics. Another consideration of focus is about having a deep analysis on at least 7 functions. Nowadays, advanced statistic models and algorithm ca be used widely, including in advertising and other marketing measures. Later on this subtopic, there are examples that sucessfull analytics competitors can’t be done by the organization alone, it also needs to help their vendors and customers.

THE RIGHT CULTURE: TO JUSTIFY EVERYTHING QUICKLY

The right culture to have is the culture to appreciate usage of data, fact and the things between that and the procedure to get it. It also applied in organization with high creativity and intrapreneurship: any innovation should be made based on evidence. However, always justify everything also have payoff: it might be taking long time and costly, so the managers hould balance them in order to make quick decisions.

THE RIGHT PEOPLE: THE BEST OF THEM

Analytics competitors hires best people on analytics, bunch of them, to do the analytic-based decisions and make it seamlessly in line with the business. But, the people to do the analytics just as good as how far they can communicate it, so they must have sort of good interpersonal skills. In terms of formula, it might look like this:

Good Analyst = Expertise +Ablity to express it in simple way + Interpersonal skills

Of course, to get people with this quality is not easy, not to mention taking long waiting time. To have an overseas employee might be a good idea.

THE RIGHT TECHNOLOGY: THREE PILLARS

Analytics and IT are unseparable. It is supported by three pillars: First, THE DATA,whether it is from ERP, CRM, POS, any of them, and a lot of them, means years of data. They put it in data warehouse, which a familiar tools on BI. Second, THE BI SOFTWARE, to collect data from warehouses, analyse them and making reports. And Last, THE COMPUTING HARDWARE which enables a computation power for huge volume of data, quickly.

THE (LONG) ROAD AHEAD

Well, it might be not long road, as Davenport writhe the articles 5 years before this review written in the late 2011. He was concluding his paper with reminding us that to become an analytics competitor will takes a long time until the ROI, while meantime, it will cost many efforts and expenses. Yet, it can be done gradually from current time by collecting data and refining the system, and equip the organization with analytics-minded people.

COMMENTARY

Business analytics might be an interestring concept to explore to enrich our current knowledge and view on today’s business intelligence. In contrast with BI, business analytics focuses on gaining insights and overview of organizational performance based on data and statistical methods, supported by BI applications. It also cover the issues of leadership, culture and having a certain quality of analyst within the organization. On the article, Davenport gives the readers a comprehensive look of business analytics without losing the big picture. His writing also well supported with examples which gives personal and easy-to-digest touch on complex concept. A worth to read for BI enthusiasts.

Based on a Harvard Business Review Article Titled “Competing on Analytics” by Thomas H. Davenport Published on January 2006, Article Review By Akhmad Rahadian Hutomo for Business Intelligence Assignment, Information System, Faculty of Computer Science, Universitas Indonesia on October 2011.

Some say that data is the new Oil, and I guess if we look at the value of data rather than the sustainability and availability of data then there may be some truth in this. But not everyone does see the value of data, some will see it as nothing more than a costly problem rather than as a valuable resource.

It’s estimated that enterprise data is going to grow at 10% per month or 650% over 5 years and that over 10 billion devices will be connected to the internet creating data by 2015. Amazingly globally every 2 Days we create as much Information as we did between the dawn of civilisation and 2003, and every minute of every day 24hours worth of video is uploaded to YouTube.

Perhaps what’s even more amazing is that 43% of businesses do nothing with this growing resource other than run reports on how their business performed last week/month/year. The problem is only going to get worse with over 15 petabytes of new information being created every day. For those who use social media the “share” and “RT” buttons encourage us to share posts, links, videos, images and documents creating copies of copies of copies – just like the Gemino spell in Harry Potter and The Deathly Hallows.

So if 43% of companies do nothing with data what about the 57% that do? Reports suggest that these companies are 4.5 times more likely to outperform their lower performing counterparts and the evidence is clear to those that look into it. The biggest employer in Europe, The NHS, used BI to track the spread of Flu during the National Flu Pandemic creating over 1.5 billion records in a 16 week period. Tesco’s, Coca Cola, oil companies and McDonalds all use Business Intelligence tools to gain a clearer insight into the data within their business and use that data to make informed and timely business decisions that distinguish them from the competition. Whilst the argument for using the ever increasing amounts of data for our competitive advantage doesn’t come across to much resistance the subject of data quality is beginning to generate more airtime and momentum.

Many companies just don’t have the time, resource or knowledge to look at the quality of their data and who can blame them, it’s a highly competitive space we are in, budgets are stripped of fat, head counts are slimmed down to the core and it’s all hands on deck for the primary function of the business. Interestingly the smallest amount of bad data can have sizable ramifications to the business, its customers, reputation and bottom line. Thomas C. Redman explains in his book “Data Driven” that if your data is 100% accurate and one customer order requires a dozen pieces of data then the cost is £1.00. 100 orders the cost is £100.

One Hundred orders each requiring 12 pieces of data means 1,200 pieces of data are needed. An error rate of 1 percent means there are 12 errors. Chances are high that 11 or 12 of the 100 orders will be affected. If 88 good orders costs £1 (totalling £88) then 12 bad orders will cost £10 (totalling £120) and doubling the cost of sale.

Business Intelligence solutions easily identify these areas of weakness providing companies with the opportunity to reduce their bad data and exposure, all that’s left then is to address the access rights of this data.

Perhaps instead of data being the new oil we should refer to it as being the new gold, increasingly valuable and readily available to the masses.